Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “climate warming”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Summer thaw duration is a strong predictor of the soil microbiome and its response to permafrost thaw in arctic tundra

Abstract Climate warming has increased permafrost thaw in arctic tundra and extended the duration of annual thaw (number of thaw days in summer) along soil profiles. Predicting the microbial response to permafrost thaw depends largely on knowing how increased thaw duration affects the composition of the soil microbiome. Here, we determined soil microbiome composition from the annually thawed surface active layer down through permafrost from two tundra types at each of three sites on the North Slope of Alaska, USA. Variations in soil microbial taxa were found between sites up to ~90 km apart, between tundra types, and between soil depths. Microbiome differences at a site were greatest across transitions from thawed to permafrost depths. Results from correlation analysis based on multi‐decadal thaw surveys show that differences in thaw duration by depth were significantly, positively correlated with the abundance of dominant taxa in the active layer and negatively correlated with dominant taxa in the permafrost. Microbiome composition within the transition zone was statistically similar to that in the permafrost, indicating that recent decades of intermittent thaw have not yet induced a shift from permafrost to active‐layer microbes. We suggest that thaw duration rather than thaw frequency has a greater impact on the composition of microbial taxa within arctic soils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Detecting forest response to droughts with global observations of vegetation water content

Droughts in a warming climate have become more common and more extreme, making understanding forest responses to water stress increasingly pressing. Analysis of water stress in trees has long focused on water potential in xylem and leaves, which influences stomatal closure and water flow through the soil-plant-atmosphere continuum. At the same time, changes of vegetation water content (VWC) are linked to a range of tree responses, including fluxes of water and carbon, mortality, flammability, and more. Unlike water potential, which requires demanding in situ measurements, VWC can be retrieved from remote sensing measurements, particularly at microwave frequencies using radar and radiometry. Here, we highlight key frontiers through which VWC has the potential to significantly increase our understanding of forest responses to water stress. To validate remote sensing observations of VWC at landscape scale and to better relate them to data assimilation model parameters, we introduce an ecosystem-scale analogue of the pressure-volume curve, the non-linear relationship between 44 average leaf or branch water potential and water content commonly used in plant hydraulics. The 45 sources of variability in these ecosystem-scale pressure-volume curves and their relationship to 46 forest response to water stress are discussed. We further show to what extent diel, seasonal, and 47 decadal dynamics of VWC reflect variations in different processes relating the tree response to 48 water stress. VWC can also be used for inferring belowground conditions – which are difficult to 49 impossible to observe directly. Lastly, we discuss how a dedicated geostationary spaceborne 50 observational system for VWC, when combined with existing datasets, can capture diel and 51 seasonal water dynamics to advance the science and applications of global forest vulnerability to 52 future droughts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Habitat Affinity of Riverine Dissolved Organic Matter Linked to Molecular Traits

Fluxes of organic matter across Earth habitats shape ecosystem function and carbon storage. Yet, it is challenging to predict howthe molecules are preferentially accumulated in specific habitats, that is, the habitat affinities, due to their diverse character-istics. Here, we develop an indicator of compositional-level habitat affinity for dissolved organic matter (DOM) by quantifyingthe aggregated affinities of individual molecules that are over- or under-represented relative to a reference habitat. Applyingthis indicator to 93 paired riverine water-sediment sites, we find that 65.1% of molecules show non-significant habitat affinitiesfor water or sediment and are thermodynamically favorable and susceptible to microbial degradation. Consistently, the habitataffinities of DOM assemblages decrease with lower molecular weights and recalcitrance for waters, and with higher thermody-namic favorability for sediments. These affinities are both reduced by elevated temperatures and anthropogenic pressures, suchas nitrogen loading and land-use change. Our projection of habitat affinities of global riverine DOM shows that regions withintense anthropogenic disturbances or warm climates, such as East Asia, Western Europe, and the Amazon, tend to exhibit loweraffinities. These findings suggest enhanced molecular homogenization between water and sediment habitats is associated withenvironmental changes and underscore the importance of habitat affinities for predicting the potential trajectories of organiccarbon across ecosystems

Cui, Yifan↗
DOE OSTI2024

Increasing thermostability of the key photorespiratory enzyme glycerate 3‐kinase by structure‐based recombination

As global temperatures rise, improving crop yields will require enhancing the thermotolerance of crops. One approach for improving thermotolerance is using bioengineering to increase the thermostability of enzymes catalysing essential biological processes. Photorespiration is an essential recycling process in plants that is integral to photosynthesis and crop growth. The enzymes of photorespiration are targets for enhancing plant thermotolerance as this pathway limits carbon fixation at elevated temperatures. We explored the effects of temperature on the activity of the photorespiratory enzyme glycerate kinase (GLYK) from various organisms and the homologue from the thermophilic alga Cyanidioschyzon merolae was more thermotolerant than those from mesophilic plants, including Arabidopsis thaliana. To understand enzyme features underlying the thermotolerance of C. merolae GLYK (CmGLYK), we performed molecular dynamics simulations using AlphaFold-predicted structures, which revealed greater movement of loop regions of mesophilic plant GLYKs at higher temperatures compared to CmGLYK. Based on these simulations, hybrid proteins were produced and analysed. These hybrid enzymes contained loop regions from CmGLYK replacing the most mobile corresponding loops of AtGLYK. Two of these hybrid enzymes had enhanced thermostability, with melting temperatures increased by 6 °C. One hybrid with three grafted loops maintained higher activity at elevated temperatures. Whilst this hybrid enzyme exhibited enhanced thermostability and a similar Km for ATP compared to AtGLYK, its Km for glycerate increased threefold. This study demonstrates that molecular dynamics simulation-guided structure-based recombination offers a promising strategy for enhancing the thermostability of other plant enzymes with possible application to increasing the thermotolerance of plants under warming climates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A spatial evaluation of Arctic sea ice and regional limitations in CMIP6 historical simulations

The Arctic sea ice response to a warming climate is assessed in a subset of models participating in Phase 6 of the Coupled Model Intercomparison Project (CMIP6), using several metrics in comparison with satellite observations and results from the Pan-Arctic Ice Ocean Modeling and Assimilation System and the Regional Arctic System Model. Our study examines the historical representation of sea ice extent, volume, and thickness using spatial analysis metrics, such as the integrated ice-edge error, Brier score, and Spatial Probability Score. We find that the CMIP6 multi-model mean captures the mean annual cycle and 1979-2014 sea ice trends remarkably well. However, individual models experience a wide range of uncertainty in the spatial distribution of sea ice when compared against satellite measurements and reanalysis data. Our metrics expose common and individual regional model biases, which sea ice temporal analyses alone do not capture. We identify large ice edge and ice thickness errors in Arctic sub-regions, implying possible model specific limitations in or lack of representation of some key physical processes. We postulate that many of them could be related to the oceanic forcing, especially in the marginal and shelf seas, where seasonal sea ice changes are not adequately simulated. We therefore conclude that an individual model’s ability to represent the observed/reanalysis spatial distribution still remains a challenge. We propose the spatial analysis metrics as useful tools to diagnose model limitations, narrow down possible processes affecting them, and guide future model improvements critical to the representation and projections of Arctic climate change.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Assessment of the Pan-Arctic Accelerated Rate of Sea Ice Decline in CMIP6 Historical Simulations

Abstract Arctic sea ice loss in response to a warming climate is assessed in 42 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Sea ice observations show a significant acceleration in the rate of decline commencing near the turn of the twenty-first century. It is our assertion that state-of-the-art climate models should qualitatively reflect this accelerated trend within the limitations of internal variability and observational uncertainty. Our analysis shows that individual CMIP6 simulations of sea ice depict a wide range of model spread on biases and anomaly trends both across models and among their ensemble members. While the CMIP6 multimodel mean captures the observed sea ice area (SIA) decline relatively well, an individual model’s ability to represent the acceleration in sea ice decline remains a challenge. Seventeen (40%) out of 42 CMIP6 models and 37 (13%) out of the total 286 ensemble members reasonably capture the observed trends and acceleration in SIA decline. In addition, a larger ensemble size appears to increase the odds for a model to include at least one ensemble member skillfully representing the accelerated SIA trends. Simulations of sea ice volume (SIV) show much larger spread and uncertainty than SIA; however, due to limited availability of sea ice thickness data, these are not as well constrained by observations. Finally, we find that models with more ocean heat transport simulate larger sea ice declines, which suggests an emergent constraint in CMIP6 ensembles. This relationship points to the need for better understanding and modeling of ice–ocean interactions, especially with respect to frazil ice growth.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Characterizing warm atmospheric boundary layer over land by combining Raman and Doppler lidar measurements

PBL plays a critical role in the atmosphere by transferring heat, moisture, and momentum. The warm PBL has a distinct diurnal cycle including daytime convective mixing layer (ML) and nighttime residual layer developments. Thus, for PBL characterization and process study, simultaneous determinations of PBL height (PBLH) and ML height (MLH) are necessary. Here, new approaches are developed to provide reliable PBLH and MLH to characterize warm PBL evolution. The approaches use Raman lidar (RL) water vapor mixing ratio (WVMR) and Doppler lidar (DL) vertical velocity measurements at the Southern Great Plains (SGP) atmospheric observatory, which was established by the Atmospheric Radiation Measurement (ARM) user facility. Compared with widely used lidar aerosol measurements for PBLH, WVMR is a better trace for PBL vertical mixing. For PBLH, the approach classifies PBL water vapor structures into a few general patterns, then uses a slope method and dynamic threshold method to determine PBLH. For MLH, wavelet analysis is used to re-construct 2-D variance from DL vertical wind velocity measurements according to the turbulence eddy size to minimize the impacts of gravity wave and eddy size on variance calculations; then, a dynamic threshold method is used to determine MLH. Remotely-sensed PBLHs and MLHs are compared with radiosonde measurements based on the Richardson number method. Good agreements between them confirm that the proposed new algorithms are reliable for PBLH and MLH characterization. The algorithms are applied to warm seasons’ RL and ML measurements at the SGP site for five years to study warm season PBL structure and processes. The weekly composited diurnal evolutions of PBLHs and MLHs in warm climate were provided to illustrate diurnal and seasonal PBL evolutions. This reliable PBLH and MLH dataset will be valuable for PBL process study, model evolution, and PBL parameterization improvement.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Influence of open ocean biogeochemistry on aerosol and clouds: Recent findings and perspectives

Aerosols and clouds are key components of the marine atmosphere, impacting the Earth’s radiative budget with a net cooling effect over the industrial era that counterbalances greenhouse gas warming, yet with an uncertain amplitude. Here we report recent advances in our understanding of how open ocean aerosol sources are modulated by ocean biogeochemistry and how they, in turn, shape cloud coverage and properties. We organize these findings in successive steps from ocean biogeochemical processes to particle formation by nucleation and sea spray emissions, further particle growth by condensation of gases, the potential to act as cloud condensation nuclei or ice nucleating particles, and finally, their effects on cloud formation, optical properties, and life cycle. We discuss how these processes may be impacted in a warming climate and the potential for ocean biogeochemistry—climate feedbacks through aerosols and clouds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Amanzi–ATS: Modeling Environmental Systems across Scales [Brief]

Department of Energy national labs (Los Alamos, Oak Ridge, Lawrence Berkeley, and Pacific Northwest) designed Amanzi-ATS to model complex environmental systems across multiple scales. The open-source software includes the most complete suite of surface/subsurface processes, allowing users to select physical processes and their coupling interactions without rewriting software. Amanzi–ATS has been used to analyze pristine local watersheds, wildfire impact on watersheds, subsurface contaminant transport at legacy waste sites, the effect of a warming climate on the Arctic tundra, and groundwater in fractured porous media. Department of Energy national labs, U.S. Geological Survey, academia, and industry have applied the software.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

AI Scaling Laws for Extremes (AISLE)

Focal Area(s) Using artificial intelligence to understand the physics of hydrologic events across temporal and spatial scales, to assess their representation in climate models, and to predict their response to climate warming. This covers both the prescribed foci of (2) predictive modeling using a hierarchy of models and (3) insight gleaned from complex data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Represent precipitation-induced geological hazards in Earth system models using artificial intelligence

Precipitation-induced geological hazards, such as debris flow, landslides, mudflow and rockfalls (hereafter referred to as landslides), pose serious threats to public safety in many areas through the world. As residential properties and infrastructure in the US have increasingly expanded into landslide-prone areas and the drivers of landslides (e.g., wildfires and hurricanes) are predicted to intensify under climate warming, losses and fatalities from landslides are likely to increase in the future. Although our understanding of geoenvironmental factors and mechanisms contributing to landslides has greatly improved, only moderate progress has been made in predicting landslides out of well-studied watersheds. Furthermore, explicitly representing various landslide-related processes in Earth system models (ESMs), from the buckling of local bearing elements in granular materials, to frictional sliding between grains, formation of microcracks in the soil matrix, rupture of capillary bridges, or breakage of plant roots3 is still very unlikely within the next decade, even with the help of exascale computers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Electricity demand in South Asia – data gaps and pathways for research and modeling

South Asia is one of the fastest growing regions - economically and energy-wise in the world. However, there are significant uncertainties in how the energy demand would grow given the rapidly rising incomes, urbanization, industrialization, access to energy, warming climate and technological change. The transition to clean energy in the region is a priority and is critical to limiting emissions in the region and increasing regional energy security. The power sector will play an important role in decarbonization of energy systems, lowering emissions in other sectors through electrification. However, as existing end uses are scaled up more and new end uses are electrified, the temporal and spatial patterns of electricity consumption could change. These changes have implications for the magnitude, shape, and timing of peak demand, which in turn affects the power sector’s investments and operations, and ability to provide reliable power. In this report, we outline key uncertainties in demand growth that South Asian countries would face in the near to medium term, along with summarizing insights from stakeholder consultations we held over the past 6 months.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Distributed Renewables for Arctic Energy: A Case Study

Alaska is a vast state that stretches into the Arctic Circle. Roughly 140,000 people in the state are dependent on isolated electric grids, traditionally burning expensive fossil fuels. This has negative impacts on air quality and climate. As the climate warms, fuel supply chains and traditional ways of life are threatened. Renewable electric sources offer a clean, resilient alternative with less volatile costs, but there are a variety of technical, social, economic, and political challenges to developing renewable energy systems in remote Arctic communities. Examples include harsh operating conditions, lack of local technical and managerial capacity, complex funding mechanisms, and glacial permitting processes. In this study, we interview one group of communities that are interested in adding renewable energy to their systems to understand the needs and challenges they face, and then another group that has successfully installed renewable energy, to understand how they overcame such challenges and the lessons they learned. Notable results include the importance of local buy-in, education, and technical involvement, procuring external funding sources, inter-community collaboration, installing bespoke systems, and working with reliable equipment suppliers. The goal of this report is to orient and inspire Arctic communities that want to begin their renewable transition, by providing helpful examples and points of contact.

17 WIND ENERGY↗
DOE OSTI2023

Scaling from Flux Towers to Ecosystem Models: Regional Constraints on Carbon Cycle Processes from Atmospheric Carbonyl Sulfide (Final Report)

DOE supported research suggests that gross primary productivity (GPP) is largely underestimated by global earth system models [Welp et al., 2011], reflecting the persistent challenge in extrapolating from local-scale GPP observations to global-scale earth system models. This poor understanding of GPP at large spatial scales is of particular concern in tropical forests. In tropical forests, some earth systems models forecast a powerful feedback between a warming climate and a decline in GPP resulting in forest dieback. While this simulated feedback is intensely debated, we lack robust large-scale constraints on GPP that are needed to resolve this debate. In particular, carbon dioxide measurements provide valuable information on net carbon flux, but not on the gross flux associated with GPP. Here we conducted a study of regional-to-global scale GPP using atmospheric carbonyl sulfide to provide a new constraint on GPP mechanisms in earth system models. Our project activities integrated modeling, in situ measurement, and remote sensing techniques to resolve GPP for the Amazon as well as global scale trends. The results of this work included initiating airborne carbonyl sulfide monitoring in the Amazon, training for postdocs and graduate students at a Hispanic Serving Institution, fundamental advances in carbonyl sulfide budgets [e.g. Hilton et al., Nature Climate Change, 2017], and high-profile publications that focused on GPP trends for the Amazon [Stinecipher et al., GRL, 2022] and global historical GPP trends [Campbell, et al., Nature, 2017]. Based on the suggestion of our DOE program manager, we published a state-of-the-science commentary to the scientific community on GPP monitoring with COS [Campbell et al., EOS, 2017] which was selected as the cover story. DOE support was acknowledged in all reports. The importance of this research to understanding climate change was communicated to the general public through community seminars (Rotary, Public Libraries, State Parks), an op-ed (SF Chronicle), and interviews in the mass media including two stories in the New York Times (4/5/17; 7/30/18), one of which was especially widely read after it was featured in the New York Time’s Quote of the Day.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Cas9 deletion of lutein biosynthesis in the marine alga Picochlorum celeri reduces photosynthetic pigments while sustaining high biomass productivity

Domestication of algae for food and renewable biofuels remains limited by the low photosynthetic efficiencies of processes that have evolved to be competitive for optimal light capture, incentivizing the development of large antennas in light-limiting conditions, thus decreasing efficient light utilization in cultivated ponds or photobioreactors. Reducing the pigment content to improve biomass productivity has been a strategy discussed for several decades and the ability to reduce pigment significantly is now fully at hand thanks to the widespread use of genome editing tools. Picochlorum celeri is one of the fastest growing marine algae identified and holds particular promise for outdoor cultivation, especially in saline water and warm climates. We show that while chlorophyll b is essential to sustain high biomass productivities under dense cultivation, removing Picochlorum celeri’s main carotenoid, lutein, leads to a decreased total chlorophyll content, higher a/ b ratio, reduced functional LHCII cross section and higher maximum quantum efficiencies at lower light intensities, resulting in an incremental increase in biomass productivity and increased PAR-to-biomass conversion efficiency. These findings further strengthen the existing strategies to improve photosynthetic efficiency and biomass production in algae.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Evaluating Basin-Scale Forest Adaptation Scenarios: Wildfire, Streamflow, Biomass, and Economic Recovery Synergies and Trade-Offs

Active forest management is applied in many parts of the western United States to reduce wildfire severity, mitigate vulnerability to drought and bark beetle mortality, and more recently, to increase snow retention and late-season streamflow. A rapidly warming climate accelerates the need for these restorative treatments, but the treatment priority among forest patches varies considerably. We simulated four treatment scenarios across the 3,450 km 2 Wenatchee River basin in eastern Washington, United States. We used a decision support tool (DST) to assess trade-offs and synergies within and among treatments on wildfire risk and smoke emissions, water yield and snow retention, biomass production, and economic return. Treatment scenarios emphasized prescribed burning ( BurnOnly ), biomass production ( MaxBiomass ), gap-based thinning to optimize water yield ( IdealWater ), and a principle-based restoration scenario ( RA1 ). Fire hazard, smoke emissions, and biomass production metrics were evaluated across scenarios using the Forest Vegetation Simulator, and water yields were modeled using the Distributed Hydrology Soil Vegetation Model. Simulations were summarized to both patch- (10 1 –10 2 ha) and subwatershed- (10 3 –10 4 ha) scales, and treatment effects were evaluated against an untreated baseline landscape. We used logic models to rank effect sizes by scenario across metrics along a continuum between −1 (no or weak effect) to +1 (large effect). All treatments produced benefits across one or more ecosystem services and led to synergistic benefits to water yield and wildfire hazard reduction. Tradeoffs among resource benefits were clear in wilderness where reliance on prescribed burning without mechanical treatment increased costs and eliminated the potential for biomass recovery. The BurnOnly scenario improved fire risk metrics and streamflow, but effect sizes were lower compared to other treatments. IdealWater showed the strongest benefits overall, demonstrating the ability to capture multiple resource benefits through spatially explicit thinning. Our study provides a framework for integrating strategic and tactical models that evaluate tradeoffs and synergies gained through varied management approaches. We demonstrate the utility of decision support modeling to enhance management synergies across large landscapes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The Coupled Influence of Thermal Physiology and Biotic Interactions on the Distribution and Density of Ant Species along an Elevational Gradient

A fundamental tenet of biogeography is that abiotic and biotic factors interact to shape the distributions of species and the organization of communities, with interactions being more important in benign environments, and environmental filtering more important in stressful environments. This pattern is often inferred using large databases or phylogenetic signal, but physiological mechanisms underlying such patterns are rarely examined. We focused on 18 ant species at 29 sites along an extensive elevational gradient, coupling experimental data on critical thermal limits, null model analyses, and observational data of density and abundance to elucidate factors governing species’ elevational range limits. Thermal tolerance data showed that environmental conditions were likely to be more important in colder, more stressful environments, where physiology was the most important constraint on the distribution and density of ant species. Conversely, the evidence for species interactions was strongest in warmer, more benign conditions, as indicated by our observational data and null model analyses. Our results provide a strong test that biotic interactions drive the distributions and density of species in warm climates, but that environmental filtering predominates at colder, high-elevation sites. Such a pattern suggests that the responses of species to climate change are likely to be context-dependent and more specifically, geographically-dependent.

54 ENVIRONMENTAL SCIENCES↗